Analog chip runs LLM attention 100x faster than H100 using 70,000x less power, Nature paper claims

anselm · x · 2026-09-25

• Researchers published a Nature paper on GainCellAttention, an analog in-memory computing architecture claimed to run LLM attention 100x faster than an H100 while using 70,000x less power.
• The pitch: every AI chip today inherits the von Neumann bottleneck — compute and memory are physically separated, and shuttling data back and forth can cost up to 10,000x more energy than the math itself.
• Using emerging "gain cell" hardware, the architecture performs the attention computation directly inside memory, eliminating the data movement bottleneck at its root.
• Still a research-stage result, but if it matures it could reshape inference economics; watch for follow-up on scalability and precision.

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